Forward-looking: Nvidia CEO Jensen Huang unveiled a robust lineup of AI-accelerating GPUs at the company's 2025 GPU Technology Conference, including the Blackwell Ultra B300, Vera Rubin, and Rubin Ultra. These GPUs are designed to enhance AI performance, particularly in inference and training tasks. The Blackwell Ultra B300, set for release in the second half of 2025, increases memory capacity from 192GB to 288GB of HBM3e and offers a 50% boost in dense FP4 tensor compute compared to the Blackwell GB200.These enhancements support larger AI models and improve inference performance for frameworks such as DeepSeek R1. In a full NVL72 rack configuration, the Blackwell Ultra will deliver 1.1 exaflops of dense FP4 inference compute, marking a significant leap over the current Blackwell B200 setup.The Blackwell Ultra B300 isn't just a standalone GPU. Alongside the core B300 unit, Nvidia is introducing new B300 NVL16 server rack solutions, the GB300 DGX Station, and the GB300 NV72L full rack system.Combining eight NV72L racks forms the complete Blackwell Ultra DGX SuperPOD (pictured above), featuring 288 Grace CPUs, 576 Blackwell Ultra GPUs, 300TB of HBM3e memory, and an impressive 11.5 ExaFLOPS of FP4 compute power. These systems can be interconnected to create large-scale supercomputers, which Nvidia is calling "AI factories."Initially teased at Computex 2024, next-gen Vera Rubin GPUs are expected to launch in the second half of 2026, delivering substantial performance improvements, particularly in AI training and inference. // Related StoriesVera Rubin features tens of terabytes of memory and is paired with a custom Nvidia-designed CPU, Vera, which includes 88 custom Arm cores with 176 threads.The GPU integrates two chips on a single die, achieving 50 petaflops of FP4 inference performance per chip. In a full NVL144 rack setup, Vera Rubin can deliver 3.6 exaflops of FP4 inference compute.Building on Vera Rubin's architecture, Rubin Ultra is slated for release in the second half of 2027. It will utilize the NVL576 rack configuration, with each GPU featuring four reticle-sized dies, delivering 100 petaflops of FP4 precision per chip.Rubin Ultra promises 15 exaflops of FP4 inference compute and 5 exaflops of FP8 training performance, significantly surpassing Vera Rubin's capabilities. Each Rubin Ultra GPU will include 1TB of HBM4e memory, contributing to 365TB of fast memory across the entire rack.Nvidia also introduced a next-generation GPU architecture called "Feynman," expected to debut in 2028 alongside the Vera CPU. While details remain scarce, Feynman is anticipated to further advance Nvidia's AI computing capabilities.During his keynote, Huang outlined Nvidia's ambitious vision for AI, describing data centers as "AI factories" that produce tokens processed by AI models. He also highlighted the potential for "physical AI" to power humanoid robots, leveraging Nvidia's software platforms to train AI models in virtual environments for real-world applications.Nvidia's roadmap is happy to position these GPUs as pivotal in the future of computing, emphasizing the need for increased computational power to keep pace with AI advancements. This strategy comes as Nvidia aims to reassure investors following recent market fluctuations, building on the success of its Blackwell chips.